Interpretive Summary: Shiga toxin-producing Escherichia coli (STEC) are a type of enterohemorrhagic E. coli (EHEC) bacteria that could cause illness ranging from mild intestinal disease to severe kidney complications. Other types of EHEC include the relatively important serotype E. coli O157, and more than 100 other non-O157 strains such as O26, O45, O103, O111, O121 and O145. Those serotypes are recognized recently as serious outbreak to cause human illness due to their toxicity. Although a conventional microbiological method for cell counting is still accurate, rapid methods for foodborne pathogen detection are needed for better performance. As one of optical detection methods, hyperspectral microscopic imaging can be an effective tool for identifying pathogenic bacteria because of its capability to characterize bacterial cells from microcolony samples, which could save incubation time for cell grow. The objective of this research is to develop a hyperspectral microscopic imaging method to evaluate spectral characteristics of foodborne pathogen specifically STEC. In this research, the acousto-optic tunable filters (AOTF)-based hyperspectral microscope imaging method to identify STEC serotypes with classification algorithms including support vector machine (SVM) and sparse kernel-based ensemble learning (SKEL) were presented.

Technical Abstract:
Non-O157 Shiga toxin-producing Escherichia coli (STEC) strains such as O26, O45, O103, O111, O121 and O145 are recognized as serious outbreak to cause human illness due to their toxicity. A conventional microbiological method for cell counting is laborious and needs long time for the results. Since optical detection method is promising for real-time, in-situ foodborne pathogen detection, acousto-optic tunable filters (AOTF) hyperspectral microscopic imaging (HMI) method has been developed for identifying pathogenic bacteria because of its capability to differentiate both spatial and spectral characteristics of each bacterial cell from microcolony samples. Using the AOTF-based HMI method, 89 contiguous spectral images can be acquired within approximately 30 seconds with 250 ms exposure time. From this research, we have successfully developed the protocol for live-cell immobilization on glass slides to acquire quality spectral images from STEC bacterial cells using the modified dry method. Among the contiguous spectral imagery between 450 and 800 nm, the intensity of spectral images at 458, 498, 522, 546, 570, 586, 670 and 690 nm were distinctive for STEC bacteria. Using two different classification algorithms, support vector machine (SVM) and sparse kernel-based ensemble learning (SKEL), the STEC serotype O45 can be classified with 92% detection accuracy.